ART: A machine learning Automated Recommendation Tool for synthetic biology
This tool addresses inefficiencies in synthetic biology for researchers and engineers, though it appears incremental as it builds on existing machine learning methods applied to a specific domain.
The paper tackles the challenge of long development times in synthetic biology by introducing ART, an Automated Recommendation Tool that uses machine learning and probabilistic modeling to recommend strains for bioengineering, demonstrating its capabilities on simulated and experimental data for products like biofuels and fatty acids.
Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.